Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into gen_nccl_id_op
typhoonzero
7 years ago
commit
a529d790b6
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#!/bin/bash
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# Update to point to the source file.
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VGG_SRC="vgg16_fluid.py"
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export TRAINING_ROLE=PSERVER
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export TRAINERS=2
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export POD_IP=127.0.0.1
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export PADDLE_INIT_PORT=6174
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MKL_NUM_THREADS=1 python -u ${VGG_SRC} --local 0 --ps_host=127.0.0.1:6174 --trainer_hosts=127.0.0.1:6174 &
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# Need to wait for the ps to start first.
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sleep 10
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echo "done start ps"
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export TRAINING_ROLE=TRAINER
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export TRAINERS=2
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export POD_IP=127.0.0.1
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export PADDLE_INIT_PORT=6174
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CUDA_VISIBLE_DEVICES=4 MKL_NUM_THREADS=1 python -u ${VGG_SRC} --local 0 --ps_host=127.0.0.1:6174 --trainer_hosts=127.0.0.1:6174 --device=GPU --task_index=0 &
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CUDA_VISIBLE_DEVICES=5 MKL_NUM_THREADS=1 python -u ${VGG_SRC} --local 0 --ps_host=127.0.0.1:6174 --trainer_hosts=127.0.0.1:6174 --device=GPU --task_index=1 &
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*.inference.model
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Load Diff
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#!/bin/bash
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BUILD_PATH=/paddle/fp16_build
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WHEEL_PATH=$BUILD_PATH/python/dist
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INFER_PATH=$BUILD_PATH/paddle/fluid/inference/tests/book
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DEMO_PATH=/paddle/contrib/float16
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# Use the single most powerful CUDA GPU on your machine
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export CUDA_VISIBLE_DEVICES=0
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# Build the PaddlePaddle Fluid wheel package and install it.
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mkdir -p $BUILD_PATH && cd $BUILD_PATH
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cmake .. -DWITH_AVX=OFF \
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-DWITH_MKL=OFF \
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-DWITH_GPU=ON \
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-DWITH_TESTING=ON \
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-DWITH_TIMER=ON \
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-DWITH_PROFILER=ON \
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-DWITH_FLUID_ONLY=ON
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make -j `nproc`
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pip install -U "$WHEEL_PATH/$(ls $WHEEL_PATH)"
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cd $DEMO_PATH
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# Clear previous log results
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rm -f *.log
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# Test the float16 inference accuracy of resnet32 on cifar10 data set
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stdbuf -oL python float16_inference_demo.py \
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--data_set=cifar10 \
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--model=resnet \
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--threshold=0.6 \
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--repeat=10 \
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2>&1 | tee -a float16_inference_accuracy.log
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# Sleep to cool down the GPU for consistent benchmarking
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sleep 2m
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# benchmarking parameters
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REPEAT=1000
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MAXIMUM_BATCH_SIZE=512
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for ((batch_size = 1; batch_size <= MAXIMUM_BATCH_SIZE; batch_size *= 2));
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do
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# Test inference benchmark of vgg16 on imagenet
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stdbuf -oL python float16_inference_demo.py \
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--data_set=imagenet \
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--model=vgg \
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--threshold=0.001 \
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--repeat=1 \
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$INFER_PATH/test_inference_image_classification_vgg \
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--data_set=imagenet \
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--dirname=$DEMO_PATH/image_classification_imagenet_vgg.inference.model \
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--fp16_dirname=$DEMO_PATH/float16_image_classification_imagenet_vgg.inference.model \
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--repeat=$REPEAT \
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--batch_size=$batch_size \
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--skip_cpu=true \
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2>&1 | tee -a imagenet_vgg16_benchmark.log
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sleep 2m
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# Test inference benchmark of resnet50 on imagenet
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stdbuf -oL python float16_inference_demo.py \
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--data_set=imagenet \
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--model=resnet \
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--threshold=0.001 \
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--repeat=1 \
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$INFER_PATH/test_inference_image_classification_resnet \
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--data_set=imagenet \
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--dirname=$DEMO_PATH/image_classification_imagenet_resnet.inference.model \
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--fp16_dirname=$DEMO_PATH/float16_image_classification_imagenet_resnet.inference.model \
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--repeat=$REPEAT \
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--batch_size=$batch_size \
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--skip_cpu=true \
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2>&1 | tee -a imagenet_resnet50_benchmark.log
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sleep 2m
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# Test inference benchmark of vgg16 on cifar10
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stdbuf -oL python float16_inference_demo.py \
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--data_set=cifar10 \
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--model=vgg \
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--threshold=0.001 \
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--repeat=1 \
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$INFER_PATH/test_inference_image_classification_vgg \
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--data_set=cifar10 \
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--dirname=$DEMO_PATH/image_classification_cifar10_vgg.inference.model \
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--fp16_dirname=$DEMO_PATH/float16_image_classification_cifar10_vgg.inference.model \
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--repeat=$REPEAT \
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--batch_size=$batch_size \
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--skip_cpu=true \
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2>&1 | tee -a cifar10_vgg16_benchmark.log
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sleep 1m
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# Test inference benchmark of resnet32 on cifar10
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stdbuf -oL python float16_inference_demo.py \
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--data_set=cifar10 \
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--model=resnet \
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--threshold=0.001 \
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--repeat=1 \
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$INFER_PATH/test_inference_image_classification_vgg \
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--data_set=cifar10 \
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--dirname=$DEMO_PATH/image_classification_cifar10_resnet.inference.model \
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--fp16_dirname=$DEMO_PATH/float16_image_classification_cifar10_resnet.inference.model \
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--repeat=$REPEAT \
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--batch_size=$batch_size \
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--skip_cpu=true \
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2>&1 | tee -a cifar10_resnet32_benchmark.log
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sleep 1m
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done
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